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Found 10 papers, 0 papers with code
Simultaneously Learning Stochastic and Adversarial Bandits with General Graph Feedback
With a general feedback graph, the observation of an arm may not be available when this arm is pulled, which makes the exploration more expensive and the algorithms more challenging to perform optimally in both environments.
Regularized-OFU: an efficient algorithm for general contextual bandit with optimization oracles
In contextual bandit, one major challenge is to develop theoretically solid and empirically efficient algorithms for general function classes.
Regularized OFU: an Efficient UCB Estimator forNon-linear Contextual Bandit
However, it is in general unknown how to deriveefficient and effective EE trade-off methods for non-linearcomplex tasks, suchas contextual bandit with deep neural network as the reward function.
Posterior sampling for multi-agent reinforcement learning: solving extensive games with imperfect information
Posterior sampling for reinforcement learning (PSRL) is a useful framework for making decisions in an unknown environment.
Lazy-CFR: fast and near-optimal regret minimization for extensive games with imperfect information
In this paper, we present Lazy-CFR, a CFR algorithm that adopts a lazy update strategy to avoid traversing the whole game tree in each round.
$A^*$ sampling with probability matching
We provide insights into the relationship between $A^*$ sampling and probability matching by analyzing a nontrivial special case in which the state space is partitioned into two subsets.
Lazy-CFR: fast and near optimal regret minimization for extensive games with imperfect information
In this paper, we present a novel technique, lazy update, which can avoid traversing the whole game tree in CFR, as well as a novel analysis on the regret of CFR with lazy update.
Online Label Aggregation: A Variational Bayesian Approach
Our evaluation results on various online scenarios show that BiLA can effectively infer the true labels, with an error rate reduction of at least 10 to 1. 5 percent points for synthetic and real-world datasets, respectively.
Racing Thompson: an Efficient Algorithm for Thompson Sampling with Non-conjugate Priors
Thompson sampling has impressive empirical performance for many multi-armed bandit problems.
Identify the Nash Equilibrium in Static Games with Random Payoffs
We study the problem on how to learn the pure Nash Equilibrium of a two-player zero-sum static game with random payoffs under unknown distributions via efficient payoff queries.
